{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "HAN.ipynb",
      "version": "0.3.2",
      "views": {},
      "default_view": {},
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "qktAMHpzO2Pa",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import pickle\n",
        "from collections import defaultdict\n",
        "import re\n",
        "from bs4 import BeautifulSoup\n",
        "import sys\n",
        "import os\n",
        "os.environ['KERAS_BACKEND']='theano'\n",
        "from keras.preprocessing.text import Tokenizer,text_to_word_sequence\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.utils.np_utils import to_categorical\n",
        "from keras.layers import Embedding\n",
        "from keras.layers import Dense, Input, Flatten\n",
        "from keras.layers import Conv1D, MaxPooling1D, Embedding, Dropout, LSTM, GRU, Bidirectional, TimeDistributed\n",
        "from keras.models import Model\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "import matplotlib.pyplot as plt\n",
        "plt.switch_backend('agg')\n",
        "from keras import backend as K\n",
        "from keras.engine.topology import Layer, InputSpec\n",
        "from keras import initializers\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "dYh0z7QmO4Fd",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "def clean_str(string):\n",
        "    string = re.sub(r\"\\\\\", \"\", string)\n",
        "    string = re.sub(r\"\\'\", \"\", string)\n",
        "    string = re.sub(r\"\\\"\", \"\", string)\n",
        "    return string.strip().lower()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Ojcss8mgPVu1",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "MAX_SENT_LENGTH = 100\n",
        "MAX_SENTS = 15\n",
        "MAX_NB_WORDS = 20000\n",
        "EMBEDDING_DIM = 100\n",
        "VALIDATION_SPLIT = 0.2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "HwpX0l5_PX6P",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "6752cb14-6065-454e-8ec1-5f1d07713469",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1530792394012,
          "user_tz": -330,
          "elapsed": 971,
          "user": {
            "displayName": "Akshat Maheshwari",
            "photoUrl": "//lh5.googleusercontent.com/-f-xJkriVoaI/AAAAAAAAAAI/AAAAAAAAAVQ/TLGa4qObGgQ/s50-c-k-no/photo.jpg",
            "userId": "114426356464940466000"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "# reading data\n",
        "df = pd.read_excel('dataset.xlsx')\n",
        "df = df.dropna()\n",
        "df = df.reset_index(drop=True)\n",
        "print('Shape of dataset ',df.shape)\n",
        "print(df.columns)\n",
        "print('No. of unique classes',len(set(df['class'])))"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of dataset  (100, 2)\n",
            "Index(['message', 'class'], dtype='object')\n",
            "No. of unique classes 17\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "BDWpT4AOPbKr",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "from nltk import tokenize\n",
        "\n",
        "reviews = []\n",
        "labels = []\n",
        "texts = []"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "s-XcxOfYPgvo",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "macronum=sorted(set(df['class']))\n",
        "macro_to_id = dict((note, number) for number, note in enumerate(macronum))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hQH-nqcHPkSS",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "def fun(i):\n",
        "    return macro_to_id[i]\n",
        "\n",
        "df['class']=df['class'].apply(fun)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CFQQzuHZPtD6",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "dc8d1570-1b81-47fd-c3d6-26068d8e603d",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1530792399003,
          "user_tz": -330,
          "elapsed": 978,
          "user": {
            "displayName": "Akshat Maheshwari",
            "photoUrl": "//lh5.googleusercontent.com/-f-xJkriVoaI/AAAAAAAAAAI/AAAAAAAAAVQ/TLGa4qObGgQ/s50-c-k-no/photo.jpg",
            "userId": "114426356464940466000"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "for i in range(df.message.shape[0]):\n",
        "    text = BeautifulSoup(df.message[i])\n",
        "    text=clean_str(str(text.get_text().encode()).lower())\n",
        "    texts.append(text)\n",
        "    sentences = tokenize.sent_tokenize(text)\n",
        "    reviews.append(sentences)\n",
        "\n",
        "\n",
        "for i in df['class']:\n",
        "    labels.append(i)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/bs4/__init__.py:181: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system (\"html5lib\"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.\n",
            "\n",
            "The code that caused this warning is on line 193 of the file /usr/lib/python3.6/runpy.py. To get rid of this warning, change code that looks like this:\n",
            "\n",
            " BeautifulSoup(YOUR_MARKUP})\n",
            "\n",
            "to this:\n",
            "\n",
            " BeautifulSoup(YOUR_MARKUP, \"html5lib\")\n",
            "\n",
            "  markup_type=markup_type))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "WzwyO0qxQC2z",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "tokenizer = Tokenizer(num_words=MAX_NB_WORDS)\n",
        "tokenizer.fit_on_texts(texts)\n",
        "\n",
        "data = np.zeros((len(texts), MAX_SENTS, MAX_SENT_LENGTH), dtype='int32')\n",
        "\n",
        "for i, sentences in enumerate(reviews):\n",
        "    for j, sent in enumerate(sentences):\n",
        "        if j< MAX_SENTS:\n",
        "            wordTokens = text_to_word_sequence(sent)\n",
        "            k=0\n",
        "            for _, word in enumerate(wordTokens):\n",
        "                if k<MAX_SENT_LENGTH and tokenizer.word_index[word]<MAX_NB_WORDS:\n",
        "                    data[i,j,k] = tokenizer.word_index[word]\n",
        "                    k=k+1\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "M9u7IPzpQU7K",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c7d191a4-3fe0-4966-b743-399a094ec637",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1530792401443,
          "user_tz": -330,
          "elapsed": 1092,
          "user": {
            "displayName": "Akshat Maheshwari",
            "photoUrl": "//lh5.googleusercontent.com/-f-xJkriVoaI/AAAAAAAAAAI/AAAAAAAAAVQ/TLGa4qObGgQ/s50-c-k-no/photo.jpg",
            "userId": "114426356464940466000"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "word_index = tokenizer.word_index\n",
        "print('No. of %s unique tokens.' % len(word_index))"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No. of 932 unique tokens.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "m-d7UKTIQcwp",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "c7a61c30-ecd3-467f-ee4e-39e883c865d3",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1530792402941,
          "user_tz": -330,
          "elapsed": 1420,
          "user": {
            "displayName": "Akshat Maheshwari",
            "photoUrl": "//lh5.googleusercontent.com/-f-xJkriVoaI/AAAAAAAAAAI/AAAAAAAAAVQ/TLGa4qObGgQ/s50-c-k-no/photo.jpg",
            "userId": "114426356464940466000"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "labels = to_categorical(np.asarray(labels))\n",
        "print('Shape of data tensor:', data.shape)\n",
        "print('Shape of label tensor:', labels.shape)\n",
        "\n",
        "\n",
        "indices = np.arange(data.shape[0])\n",
        "np.random.shuffle(indices)\n",
        "data = data[indices]\n",
        "labels = labels[indices]\n",
        "nb_validation_samples = int(VALIDATION_SPLIT * data.shape[0])"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of data tensor: (100, 15, 100)\n",
            "Shape of label tensor: (100, 17)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Qx1U4OZiQgXV",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "x_train = data[:-nb_validation_samples]\n",
        "y_train = labels[:-nb_validation_samples]\n",
        "x_val = data[-nb_validation_samples:]\n",
        "y_val = labels[-nb_validation_samples:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jaCt1zexQ7d2",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8fb183ea-910e-4cab-fd75-7088cefe8f07",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1530792416542,
          "user_tz": -330,
          "elapsed": 12425,
          "user": {
            "displayName": "Akshat Maheshwari",
            "photoUrl": "//lh5.googleusercontent.com/-f-xJkriVoaI/AAAAAAAAAAI/AAAAAAAAAVQ/TLGa4qObGgQ/s50-c-k-no/photo.jpg",
            "userId": "114426356464940466000"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "embeddings_index = {}\n",
        "f = open('glove.6B.100d.txt',encoding='utf8')\n",
        "for line in f:\n",
        "    values = line.split()\n",
        "    word = values[0]\n",
        "    coefs = np.asarray(values[1:], dtype='float32')\n",
        "    embeddings_index[word] = coefs\n",
        "f.close()\n",
        "\n",
        "print('Total %s word vectors.' % len(embeddings_index))"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total 400000 word vectors.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "rUDWPQRvQ_B2",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "embedding_matrix = np.random.random((len(word_index) + 1, EMBEDDING_DIM))\n",
        "for word, i in word_index.items():\n",
        "    embedding_vector = embeddings_index.get(word)\n",
        "    if embedding_vector is not None:\n",
        "        # words not found in embedding index will be all-zeros.\n",
        "        embedding_matrix[i] = embedding_vector\n",
        "\n",
        "embedding_layer = Embedding(len(word_index) + 1,\n",
        "                            EMBEDDING_DIM,\n",
        "                            weights=[embedding_matrix],\n",
        "                            input_length=MAX_SENT_LENGTH,\n",
        "                            trainable=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zKz6YxXoRIEe",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "outputId": "d99c8642-0990-4ff3-a351-69c63a41b636",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1530792542554,
          "user_tz": -330,
          "elapsed": 124363,
          "user": {
            "displayName": "Akshat Maheshwari",
            "photoUrl": "//lh5.googleusercontent.com/-f-xJkriVoaI/AAAAAAAAAAI/AAAAAAAAAVQ/TLGa4qObGgQ/s50-c-k-no/photo.jpg",
            "userId": "114426356464940466000"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "sentence_input = Input(shape=(MAX_SENT_LENGTH,), dtype='int32')\n",
        "embedded_sequences = embedding_layer(sentence_input)\n",
        "l_lstm = Bidirectional(LSTM(100))(embedded_sequences)\n",
        "sentEncoder = Model(sentence_input, l_lstm)\n",
        "\n",
        "review_input = Input(shape=(MAX_SENTS,MAX_SENT_LENGTH), dtype='int32')\n",
        "review_encoder = TimeDistributed(sentEncoder)(review_input)\n",
        "l_lstm_sent = Bidirectional(LSTM(100))(review_encoder)\n",
        "preds = Dense(len(macronum), activation='softmax')(l_lstm_sent)\n",
        "model = Model(review_input, preds)\n",
        "\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer='rmsprop',\n",
        "              metrics=['acc'])\n",
        "\n",
        "print(\"Hierachical LSTM\")\n",
        "model.summary()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO (theano.gof.compilelock): Waiting for existing lock by process '1011' (I am process '1779')\n",
            "INFO (theano.gof.compilelock): To manually release the lock, delete /content/.theano/compiledir_Linux-4.4.111+-x86_64-with-Ubuntu-17.10-artful-x86_64-3.6.3-64/lock_dir\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Hierachical LSTM\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_4 (InputLayer)         (None, 15, 100)           0         \n",
            "_________________________________________________________________\n",
            "time_distributed_1 (TimeDist (None, 15, 200)           254100    \n",
            "_________________________________________________________________\n",
            "bidirectional_2 (Bidirection (None, 200)               240800    \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 17)                3417      \n",
            "=================================================================\n",
            "Total params: 498,317\n",
            "Trainable params: 498,317\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "EJKKRZnYRdIw",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 1074
        },
        "outputId": "fafa35cc-0396-41fb-8faf-d994c6279fa0",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1530793151231,
          "user_tz": -330,
          "elapsed": 608660,
          "user": {
            "displayName": "Akshat Maheshwari",
            "photoUrl": "//lh5.googleusercontent.com/-f-xJkriVoaI/AAAAAAAAAAI/AAAAAAAAAVQ/TLGa4qObGgQ/s50-c-k-no/photo.jpg",
            "userId": "114426356464940466000"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "cp=ModelCheckpoint('model_han_.hdf5',monitor='val_acc',verbose=1,save_best_only=True)\n",
        "history=model.fit(x_train, y_train, validation_data=(x_val, y_val),\n",
        "          epochs=15, batch_size=2,callbacks=[cp])"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 80 samples, validate on 20 samples\n",
            "Epoch 1/15\n",
            "80/80 [==============================] - 28s 351ms/step - loss: 2.5511 - acc: 0.1875 - val_loss: 2.9483 - val_acc: 0.2500\n",
            "\n",
            "Epoch 00001: val_acc improved from -inf to 0.25000, saving model to model_han_.hdf5\n",
            "Epoch 2/15\n",
            "80/80 [==============================] - 28s 349ms/step - loss: 2.1806 - acc: 0.2750 - val_loss: 2.8415 - val_acc: 0.3500\n",
            "\n",
            "Epoch 00002: val_acc improved from 0.25000 to 0.35000, saving model to model_han_.hdf5\n",
            "Epoch 3/15\n",
            "80/80 [==============================] - 28s 350ms/step - loss: 1.6876 - acc: 0.5875 - val_loss: 3.9033 - val_acc: 0.2000\n",
            "\n",
            "Epoch 00003: val_acc did not improve from 0.35000\n",
            "Epoch 4/15\n",
            "80/80 [==============================] - 28s 351ms/step - loss: 1.3917 - acc: 0.6500 - val_loss: 2.5706 - val_acc: 0.4500\n",
            "\n",
            "Epoch 00004: val_acc improved from 0.35000 to 0.45000, saving model to model_han_.hdf5\n",
            "Epoch 5/15\n",
            "50/80 [=================>............] - ETA: 10s - loss: 0.9921 - acc: 0.7000"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "80/80 [==============================] - 28s 350ms/step - loss: 1.1223 - acc: 0.6625 - val_loss: 2.4962 - val_acc: 0.5000\n",
            "\n",
            "Epoch 00005: val_acc improved from 0.45000 to 0.50000, saving model to model_han_.hdf5\n",
            "Epoch 6/15\n",
            "80/80 [==============================] - 28s 351ms/step - loss: 0.9457 - acc: 0.7125 - val_loss: 2.4805 - val_acc: 0.4500\n",
            "\n",
            "Epoch 00006: val_acc did not improve from 0.50000\n",
            "Epoch 7/15\n",
            "80/80 [==============================] - 34s 420ms/step - loss: 0.8393 - acc: 0.7625 - val_loss: 1.9323 - val_acc: 0.7000\n",
            "\n",
            "Epoch 00007: val_acc improved from 0.50000 to 0.70000, saving model to model_han_.hdf5\n",
            "Epoch 8/15\n",
            "80/80 [==============================] - 34s 431ms/step - loss: 0.6905 - acc: 0.8375 - val_loss: 2.0766 - val_acc: 0.7000\n",
            "\n",
            "Epoch 00008: val_acc did not improve from 0.70000\n",
            "Epoch 9/15\n",
            "80/80 [==============================] - 33s 413ms/step - loss: 0.6308 - acc: 0.8250 - val_loss: 2.1420 - val_acc: 0.6500\n",
            "\n",
            "Epoch 00009: val_acc did not improve from 0.70000\n",
            "Epoch 10/15\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "80/80 [==============================] - 38s 474ms/step - loss: 0.5324 - acc: 0.8500 - val_loss: 2.0284 - val_acc: 0.7000\n",
            "\n",
            "Epoch 00010: val_acc did not improve from 0.70000\n",
            "Epoch 11/15\n",
            "80/80 [==============================] - 41s 507ms/step - loss: 0.4603 - acc: 0.8750 - val_loss: 2.2320 - val_acc: 0.6500\n",
            "\n",
            "Epoch 00011: val_acc did not improve from 0.70000\n",
            "Epoch 12/15\n",
            "80/80 [==============================] - 46s 573ms/step - loss: 0.4084 - acc: 0.8875 - val_loss: 2.2356 - val_acc: 0.6500\n",
            "\n",
            "Epoch 00012: val_acc did not improve from 0.70000\n",
            "Epoch 13/15\n",
            "80/80 [==============================] - 53s 668ms/step - loss: 0.3975 - acc: 0.8875 - val_loss: 2.4935 - val_acc: 0.6500\n",
            "\n",
            "Epoch 00013: val_acc did not improve from 0.70000\n",
            "Epoch 14/15\n",
            "80/80 [==============================] - 60s 745ms/step - loss: 0.3789 - acc: 0.8750 - val_loss: 2.1979 - val_acc: 0.7000\n",
            "\n",
            "Epoch 00014: val_acc did not improve from 0.70000\n",
            "Epoch 15/15\n",
            "14/80 [====>.........................] - ETA: 57s - loss: 0.1149 - acc: 1.0000"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "80/80 [==============================] - 68s 855ms/step - loss: 0.2603 - acc: 0.9250 - val_loss: 2.3647 - val_acc: 0.7000\n",
            "\n",
            "Epoch 00015: val_acc did not improve from 0.70000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "s2trhmd3RNkk",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "618201e7-902b-4de9-bd5e-e4a05b65c931",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1530793153471,
          "user_tz": -330,
          "elapsed": 2221,
          "user": {
            "displayName": "Akshat Maheshwari",
            "photoUrl": "//lh5.googleusercontent.com/-f-xJkriVoaI/AAAAAAAAAAI/AAAAAAAAAVQ/TLGa4qObGgQ/s50-c-k-no/photo.jpg",
            "userId": "114426356464940466000"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "fig1 = plt.figure()\n",
        "plt.plot(history.history['loss'],'r',linewidth=3.0)\n",
        "plt.plot(history.history['val_loss'],'b',linewidth=3.0)\n",
        "plt.legend(['Training loss', 'Validation Loss'],fontsize=18)\n",
        "plt.xlabel('Epochs ',fontsize=16)\n",
        "plt.ylabel('Loss',fontsize=16)\n",
        "plt.title('Loss Curves :HAN',fontsize=16)\n",
        "fig1.savefig('loss_han.png')\n",
        "plt.show()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEdCAYAAADjFntmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njEmxomCMMSbFioIxxpgUKwrGGGNS/h8y2FUT5+O88QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f73e82ecef0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ckA37QnERtzu",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "451e6f61-7eec-4904-ed24-e68adec92156",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1530793155073,
          "user_tz": -330,
          "elapsed": 1471,
          "user": {
            "displayName": "Akshat Maheshwari",
            "photoUrl": "//lh5.googleusercontent.com/-f-xJkriVoaI/AAAAAAAAAAI/AAAAAAAAAVQ/TLGa4qObGgQ/s50-c-k-no/photo.jpg",
            "userId": "114426356464940466000"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "fig2=plt.figure()\n",
        "plt.plot(history.history['acc'],'r',linewidth=3.0)\n",
        "plt.plot(history.history['val_acc'],'b',linewidth=3.0)\n",
        "plt.legend(['Training Accuracy', 'Validation Accuracy'],fontsize=18)\n",
        "plt.xlabel('Epochs ',fontsize=16)\n",
        "plt.ylabel('Accuracy',fontsize=16)\n",
        "plt.title('Accuracy Curves : HAN',fontsize=16)\n",
        "fig2.savefig('accuracy_han.png')\n",
        "plt.show()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEdCAYAAADjFntmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f73e8227780>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "gY9XUmT9k6Rb",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "#from keras.utils.vis_utils import plot_model\n",
        "#plot_model(model, to_file='han_model.png', show_shapes=True, show_layer_names=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "oIYhXM6Yoqog",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "outputId": "dcb579d9-50e0-4a9a-e16e-dc92c33cf0a1",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1530797447889,
          "user_tz": -330,
          "elapsed": 1061,
          "user": {
            "displayName": "Akshat Maheshwari",
            "photoUrl": "//lh5.googleusercontent.com/-f-xJkriVoaI/AAAAAAAAAAI/AAAAAAAAAVQ/TLGa4qObGgQ/s50-c-k-no/photo.jpg",
            "userId": "114426356464940466000"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "from PIL import Image\n",
        "display(Image.open('han_model.png'))"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAogAAAGVCAIAAADyi5c1AABpyklEQVR4nO3de1wUZf8//vdy0BSN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\nRU5ErkJggg==\n",
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=648x405 at 0x7F73E8062160>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Bc3K9kObo1Zc",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}